Abstract
Objectives
Glial fibrillary acidic protein (GFAP) is a promising biomarker that could potentially contribute to diagnosis and prognosis in neurological diseases. The biomarker is approaching clinical use but the reference interval for serum GFAP remains to be established, and knowledge about the effect of preanalytical factors is also limited.
Methods
Serum samples from 371 apparently healthy reference subjects, 21–90 years of age, were measured by a single-molecule array (Simoa) assay. Continuous reference intervals were modelled using non-parametric quantile regression and compared with traditional age-partitioned non-parametric reference intervals established according to the Clinical and Laboratory Standards Institute (CLSI) guideline C28-A3. The following preanalytical conditions were also examined: stability in whole blood at room temperature (RT), stability in serum at RT and −20 °C, repeated freeze-thaw cycles, and haemolysis.
Results
The continuous reference interval showed good overall agreement with the traditional age-partitioned reference intervals of 25–136 ng/L, 34–242 ng/L, and 5–438 ng/L for the age groups 20–39, 40–64, and 65–90 years, respectively. Both types of reference intervals showed increasing levels and variability of serum GFAP with age. In the preanalytical tests, the mean changes from baseline were 2.3% (95% CI: −2.4%, 6.9%) in whole blood after 9 h at RT, 3.1% (95% CI: −4.5%, 10.7%) in serum after 7 days at RT, 10.4% (95% CI: −6.0%, 26.8%) in serum after 133 days at −20 °C, and 10.4% (95% CI: 9.5%, 11.4%) after three freeze-thaw cycles.
Conclusions
The study establishes age-dependent reference ranges for serum GFAP in adults and demonstrates overall good stability of the biomarker.
Funding source: Department of Clinical Biochemistry, Aarhus University Hospital
Acknowledgments
The authors sincerely thank Charlotte Nørby Pedersen and laboratory technicians Katrine Bremer for organizing the collection and analysis of blood samples, and the technicians in the blood bank and blood sampling unit for their assistance in collecting the reference samples.
-
Research funding: This study was funded by the Department of Clinical Biochemistry, Aarhus University Hospital.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: Authors state no conflict of interest.
-
Informed consent: Informed consent was obtained from all individuals included in this study.
-
Ethical approval: The local Institutional Review Board deemed the study exempt from review.
References
1. Abdelhak, A, Foschi, M, Abu-Rumeileh, S, Yue, JK, D’Anna, L, Huss, A, et al.. Blood GFAP as an emerging biomarker in brain and spinal cord disorders. Nat Rev Neurol 2022;18:158–72. https://doi.org/10.1038/s41582-021-00616-3.Search in Google Scholar PubMed
2. Huebschmann, NA, Luoto, TM, Karr, JE, Berghem, K, Blennow, K, Zetterberg, H, et al.. Comparing glial fibrillary acidic protein (GFAP) in serum and plasma following mild traumatic brain injury in older adults. Front Neurol 2020;11:1054. https://doi.org/10.3389/fneur.2020.01054.Search in Google Scholar PubMed PubMed Central
3. Korley, FK, Yue, JK, Wilson, DH, Hrusovsky, K, Diaz-Arrastia, R, Ferguson, AR, et al.. Performance evaluation of a multiplex assay for simultaneous detection of four clinically relevant traumatic brain injury biomarkers. J Neurotrauma 2019;36:182–7. https://doi.org/10.1089/neu.2017.5623.Search in Google Scholar PubMed PubMed Central
4. Okonkwo, DO, Puffer, RC, Puccio, AM, Yuh, EL, Yue, JK, Diaz-Arrastia, R, et al.. Point-of-care platform blood biomarker testing of glial fibrillary acidic protein versus S100 calcium-binding protein B for prediction of traumatic brain injuries: a transforming research and clinical knowledge in traumatic brain injury study. J Neurotrauma 2020;37:2460–7. https://doi.org/10.1089/neu.2020.7140.Search in Google Scholar PubMed PubMed Central
5. Czeiter, E, Amrein, K, Gravesteijn, BY, Lecky, F, Menon, DK, Mondello, S, et al.. Blood biomarkers on admission in acute traumatic brain injury: relations to severity, CT findings and care path in the CENTER-TBI study. EBioMedicine 2020;56:102785. https://doi.org/10.1016/j.ebiom.2020.102785.Search in Google Scholar PubMed PubMed Central
6. Puspitasari, V, Gunawan, PY, Wiradarma, HD, Hartoyo, V. Glial fibrillary acidic protein serum level as a predictor of clinical outcome in ischemic stroke. Open Access Maced J Med Sci 2019;7:1471–4. https://doi.org/10.3889/oamjms.2019.326.Search in Google Scholar PubMed PubMed Central
7. Liu, G, Geng, J. Glial fibrillary acidic protein as a prognostic marker of acute ischemic stroke. Hum Exp Toxicol 2018;37:1048–53. https://doi.org/10.1177/0960327117751236.Search in Google Scholar PubMed
8. Kedziora, J, Burzynska, M, Gozdzik, W, Kubler, A, Kobylinska, K, Adamik, B. Biomarkers of neurological outcome after aneurysmal subarachnoid hemorrhage as early predictors at discharge from an intensive care unit. Neurocritical Care 2021;34:856–66. https://doi.org/10.1007/s12028-020-01110-2.Search in Google Scholar PubMed PubMed Central
9. Zheng, YK, Dong, XQ, Du, Q, Wang, H, Yang, DB, Zhu, Q, et al.. Comparison of plasma copeptin and multiple biomarkers for assessing prognosis of patients with aneurysmal subarachnoid hemorrhage. Clin Chim Acta 2017;475:64–9. https://doi.org/10.1016/j.cca.2017.10.009.Search in Google Scholar PubMed
10. Petzold, A, Keir, G, Kerr, M, Kay, A, Kitchen, N, Smith, M, et al.. Early identification of secondary brain damage in subarachnoid hemorrhage: a role for glial fibrillary acidic protein. J Neurotrauma 2006;23:1179–84. https://doi.org/10.1089/neu.2006.23.1179.Search in Google Scholar PubMed
11. Heller, C, Foiani, MS, Moore, K, Convery, R, Bocchetta, M, Neason, M, et al.. Plasma glial fibrillary acidic protein is raised in progranulin-associated frontotemporal dementia. J Neurol Neurosurg Psychiatry 2020;91:263–70. https://doi.org/10.1136/jnnp-2019-321954.Search in Google Scholar PubMed
12. Benussi, A, Ashton, NJ, Karikari, TK, Gazzina, S, Premi, E, Benussi, L, et al.. Serum glial fibrillary acidic protein (GFAP) is a marker of disease severity in frontotemporal lobar degeneration. J Alzheim Dis 2020;77:1129–41. https://doi.org/10.3233/jad-200608.Search in Google Scholar
13. Asken, BM, Elahi, FM, La Joie, R, Strom, A, Staffaroni, AM, Lindbergh, CA, et al.. Plasma glial fibrillary acidic protein levels differ along the spectra of amyloid burden and clinical disease stage. J Alzheim Dis 2020;78:265–76. https://doi.org/10.3233/jad-200755.Search in Google Scholar PubMed PubMed Central
14. Oeckl, P, Halbgebauer, S, Anderl-Straub, S, Steinacker, P, Huss, AM, Neugebauer, H, et al.. Glial fibrillary acidic protein in serum is increased in Alzheimer’s disease and correlates with cognitive impairment. J Alzheim Dis 2019;67:481–8. https://doi.org/10.3233/jad-180325.Search in Google Scholar
15. Abdelhak, A, Huss, A, Kassubek, J, Tumani, H, Otto, M. Serum GFAP as a biomarker for disease severity in multiple sclerosis. Sci Rep 2018;8:14798. https://doi.org/10.1038/s41598-018-33158-8.Search in Google Scholar PubMed PubMed Central
16. Ayrignac, X, Le Bars, E, Duflos, C, Hirtz, C, Maleska Maceski, A, Carra-Dalliere, C, et al.. Serum GFAP in multiple sclerosis: correlation with disease type and MRI markers of disease severity. Sci Rep 2020;10:10923. https://doi.org/10.1038/s41598-020-67934-2.Search in Google Scholar PubMed PubMed Central
17. Hogel, H, Rissanen, E, Barro, C, Matilainen, M, Nylund, M, Kuhle, J, et al.. Serum glial fibrillary acidic protein correlates with multiple sclerosis disease severity. Mult Scler 2020;26:210–9. https://doi.org/10.1177/1352458518819380.Search in Google Scholar PubMed
18. Saraste, M, Bezukladova, S, Matilainen, M, Sucksdorff, M, Kuhle, J, Leppert, D, et al.. Increased serum glial fibrillary acidic protein associates with microstructural white matter damage in multiple sclerosis: GFAP and DTI. Mult Scler Relat Disord 2021;50:102810. https://doi.org/10.1016/j.msard.2021.102810.Search in Google Scholar PubMed
19. Petzold, A. Glial fibrillary acidic protein is a body fluid biomarker for glial pathology in human disease. Brain Res 2015;1600:17–31. https://doi.org/10.1016/j.brainres.2014.12.027.Search in Google Scholar PubMed
20. Abdelhak, A, Hottenrott, T, Morenas-Rodríguez, E, Suárez-Calvet, M, Zettl, UK, Haass, C, et al.. Glial activation markers in CSF and serum from patients with primary progressive multiple sclerosis: potential of serum GFAP as disease severity marker? Front Neurol 2019;10:280. https://doi.org/10.3389/fneur.2019.00280.Search in Google Scholar PubMed PubMed Central
21. van Lierop, Z, Verberk, IMW, van Uffelen KWJ, Koel-Simmelink, MJA, In ’t Veld, L, Killestein, J, et al.. Pre-analytical stability of serum biomarkers for neurological disease: neurofilament-light, glial fibrillary acidic protein and contactin-1. Clin Chem Lab Med 2022;60:842–50. https://doi.org/10.1515/cclm-2022-0007.Search in Google Scholar PubMed
22. Verberk, IMW, Misdorp, EO, Koelewijn, J, Ball, AJ, Blennow, K, Dage, JL, et al.. Characterization of pre-analytical sample handling effects on a panel of Alzheimer’s disease-related blood-based biomarkers: results from the Standardization of Alzheimer’s Blood Biomarkers (SABB) working group. Alzheimer’s Dementia 2022;18:1484–97. https://doi.org/10.1002/alz.12510.Search in Google Scholar PubMed PubMed Central
23. Ondruschka, B, Woydt, L, Bernhard, M, Franke, H, Kirsten, H, Löffler, S, et al.. Post-mortem in situ stability of serum markers of cerebral damage and acute phase response. Int J Leg Med 2019;133:871–81. https://doi.org/10.1007/s00414-018-1925-2.Search in Google Scholar PubMed
24. Rezaii, PG, Grant, GA, Zeineh, MM, Richardson, KJ, Coburn, ML, Bet, AM, et al.. Stability of blood biomarkers of traumatic brain injury. J Neurotrauma 2019;36:2407–16. https://doi.org/10.1089/neu.2018.6053.Search in Google Scholar PubMed
25. Ashton, NJ, Suárez-Calvet, M, Karikari, TK, Lantero-Rodriguez, J, Snellman, A, Sauer, M, et al.. Effects of pre-analytical procedures on blood biomarkers for Alzheimer’s pathophysiology, glial activation, and neurodegeneration. Alzheimer’s Dementia 2021;13:e12168. https://doi.org/10.1002/dad2.12168.Search in Google Scholar PubMed PubMed Central
26. CLSI. Defining, establishing, and verifying reference intervals in the clinical laboratory; approved guideline. CLSI document EP28-A3c, 3rd ed. Wayne, PA: Clinical and Laboratory Standards Institute; 2008.Search in Google Scholar
27. Cornes, M, Simundic, AM, Cadamuro, J, Costelloe, SJ, Baird, G, Kristensen, GBB, et al.. The CRESS checklist for reporting stability studies: on behalf of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for the Preanalytical Phase (WG-PRE). Clin Chem Lab Med 2020;59:59–69. https://doi.org/10.1515/cclm-2020-0061.Search in Google Scholar PubMed
28. CLSI. Interference testing in clinical chemistry. CLSI guideline EP07, 3rd ed. Wayne, PA: Clinical and Laboratory Standards Institute; 2018.Search in Google Scholar
29. Meites, S. Letter: reproducibly simulating hemolysis, for evaluating its interference with chemical methods. Clin Chem 1973;19:1319. https://doi.org/10.1093/clinchem/19.11.1319a.Search in Google Scholar
30. Holmes, DT, van der Gugten, JG, Jung, B, McCudden, CR. Continuous reference intervals for pediatric testosterone, sex hormone binding globulin and free testosterone using quantile regression. J Mass Spectrom Adv Clin Lab 2021;22:64–70. https://doi.org/10.1016/j.jmsacl.2021.10.005.Search in Google Scholar PubMed PubMed Central
31. Muggeo, VMR, Sciandra, M, Tomasello, A, Calvo, S. Estimating growth charts via nonparametric quantile regression: a practical framework with application in ecology. Environ Ecol Stat 2013;20:519–31. https://doi.org/10.1007/s10651-012-0232-1.Search in Google Scholar
32. Muggeo, VMR, Torretta, F, Eilers, PHC, Sciandra, M, Attanasio, M. Multiple smoothing parameters selection in additive regression quantiles. Stat Model Int J 2021;21:428–48. https://doi.org/10.1177/1471082x20929802.Search in Google Scholar
33. Lahti, A, Petersen, PH, Boyd, JC, Rustad, P, Laake, P, Solberg, HE. Partitioning of nongaussian-distributed biochemical reference data into subgroups. Clin Chem 2004;50:891–900. https://doi.org/10.1373/clinchem.2003.027953.Search in Google Scholar PubMed
34. Hviid, CVB, Knudsen, CS, Parkner, T. Reference interval and preanalytical properties of serum neurofilament light chain in Scandinavian adults. Scand J Clin Lab Invest 2020;80:291–5. https://doi.org/10.1080/00365513.2020.1730434.Search in Google Scholar PubMed
35. Hovden Christensen, S, Vinter Bødker Hviid, C, Tranberg Madsen, A, Parkner, T, Winther-Larsen, A. Short-term biological variation of serum glial fibrillary acidic protein. Clin Chem Lab Med 2022;60:1813–19. https://doi.org/10.1515/cclm-2022-0480.Search in Google Scholar PubMed
36. Fraser, CG, Petersen, PH. Quality goals in external quality assessment are best based on biology. Scand J Clin Lab Invest Suppl 1993;212:8–9. https://doi.org/10.3109/00365519309085446.Search in Google Scholar
37. Aktas, O, Smith, MA, Rees, WA, Bennett, JL, She, D, Katz, E, et al.. Serum glial fibrillary acidic protein: a neuromyelitis optica spectrum disorder biomarker. Ann Neurol 2021;89:895–910. https://doi.org/10.1002/ana.26067.Search in Google Scholar PubMed PubMed Central
38. Chang, X, Huang, W, Wang, L, ZhangBao, J, Zhou, L, Lu, C, et al.. Serum neurofilament light and GFAP are associated with disease severity in inflammatory disorders with aquaporin-4 or myelin oligodendrocyte glycoprotein antibodies. Front Immunol 2021;12:647618. https://doi.org/10.3389/fimmu.2021.647618.Search in Google Scholar PubMed PubMed Central
39. McCrea, M, Broglio, SP, McAllister, TW, Gill, J, Giza, CC, Huber, DL, et al.. Association of blood biomarkers with acute sport-related concussion in collegiate athletes: findings from the NCAA and Department of Defense CARE Consortium. JAMA Netw Open 2020;3:e1919771. https://doi.org/10.1001/jamanetworkopen.2019.19771.Search in Google Scholar PubMed PubMed Central
40. McDonald, SJ, O’Brien, WT, Symons, GF, Chen, Z, Bain, J, Major, BP, et al.. Prolonged elevation of serum neurofilament light after concussion in male Australian football players. Biomark Res 2021;9:4. https://doi.org/10.1186/s40364-020-00256-7.Search in Google Scholar PubMed PubMed Central
41. Schindler, P, Grittner, U, Oechtering, J, Leppert, D, Siebert, N, Duchow, AS, et al.. Serum GFAP and NfL as disease severity and prognostic biomarkers in patients with aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder. J Neuroinflammation 2021;18:105. https://doi.org/10.1186/s12974-021-02138-7.Search in Google Scholar PubMed PubMed Central
42. van der Plas, E, Long, JD, Koscik, TR, Magnotta, V, Monckton, DG, Cumming, SA, et al.. Blood-based markers of neuronal injury in adult-onset myotonic dystrophy type 1. Front Neurol 2021;12:791065. https://doi.org/10.3389/fneur.2021.791065.Search in Google Scholar PubMed PubMed Central
43. Zeitlberger, AM, Thomas-Black, G, Garcia-Moreno, H, Foiani, M, Heslegrave, AJ, Zetterberg, H, et al.. Plasma markers of neurodegeneration are raised in Friedreich’s Ataxia. Front Cell Neurosci 2018;12:366. https://doi.org/10.3389/fncel.2018.00366.Search in Google Scholar PubMed PubMed Central
44. Giza, CC, McCrea, M, Huber, D, Cameron, KL, Houston, MN, Jackson, JC, et al.. Assessment of blood biomarker profile after acute concussion during combative training among US military cadets: a prospective study from the NCAA and US Department of Defense CARE Consortium. JAMA Netw Open 2021;4:e2037731. https://doi.org/10.1001/jamanetworkopen.2020.37731.Search in Google Scholar PubMed PubMed Central
45. Stevenson-Hoare, J, Heslegrave, A, Leonenko, G, Fathalla, D, Bellou, E, Luckcuck, L, et al.. Plasma biomarkers and genetics in the diagnosis and prediction of Alzheimer’s disease. Brain 2022;awac128. https://doi.org/10.1093/brain/awac128.Search in Google Scholar PubMed
46. Quanterix. Simoa® GFAP* Discovery Kit HD-1/HD-X Data Sheet; 2018. Available from: https://www.quanterix.com/wp-content/uploads/2020/12/Simoa_GFAP_Data_Sheet_HD-1_HD-X_Rev03.pdf [Accessed 31 Aug 2022].Search in Google Scholar
47. Chatterjee, P, Pedrini, S, Stoops, E, Goozee, K, Villemagne, VL, Asih, PR, et al.. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer’s disease. Transl Psychiatry 2021;11:27. https://doi.org/10.1038/s41398-020-01137-1.Search in Google Scholar PubMed PubMed Central
48. Pereira, JB, Janelidze, S, Smith, R, Mattsson-Carlgren, N, Palmqvist, S, Teunissen, CE, et al.. Plasma GFAP is an early marker of amyloid-β but not tau pathology in Alzheimer’s disease. Brain 2021;144:3505–16. https://doi.org/10.1093/brain/awab223.Search in Google Scholar PubMed PubMed Central
49. Verberk, IMW, Thijssen, E, Koelewijn, J, Mauroo, K, Vanbrabant, J, de Wilde, A, et al.. Combination of plasma amyloid beta((1-42/1-40)) and glial fibrillary acidic protein strongly associates with cerebral amyloid pathology. Alzheimer’s Res Ther 2020;12:118. https://doi.org/10.1186/s13195-020-00682-7.Search in Google Scholar PubMed PubMed Central
50. Rodrigue, KM, Kennedy, KM, Devous, MD, Sr., Rieck, JR, Hebrank, AC, Diaz-Arrastia, R, et al.. β-Amyloid burden in healthy aging: regional distribution and cognitive consequences. Neurology 2012;78:387–95. https://doi.org/10.1212/wnl.0b013e318245d295.Search in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
